google-gemini-embeddings

SKILL.md

Semantic search and RAG systems with Gemini embeddings (768-3072 dimensions, 8 task types, Cloudflare Vectorize integration).

Supports flexible output dimensions (768, 1536, 3072, or custom 128-3071) with Matryoshka learning; only 3072-dimensional embeddings are pre-normalized, others require manual normalization for accurate similarity calculations

Eight task types optimize embeddings for specific use cases: RETRIEVAL_QUERY/DOCUMENT for RAG, SEMANTIC_SIMILARITY for search, CLUSTERING for grouping, CODE_RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION, and CLASSIFICATION

Batch API processes multiple texts in one request but has documented issues: ordering corruption at >500 texts, memory limits at >10k embeddings, and rate limit anomalies; recommended batch size is <100 texts

Integrates with Cloudflare Vectorize for vector storage and includes complete RAG patterns combining query embedding, semantic search, and LLM generation; free tier allows 100 RPM with exponential backoff required for rate limiting

Google Gemini Embeddings

Complete production-ready guide for Google Gemini embeddings API

This skill provides comprehensive coverage of the gemini-embedding-001 model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering.

Table of Contents

Quick Start

gemini-embedding-001 Model

Basic Embeddings

Batch Embeddings

Task Types

RAG Patterns

Error Handling

Best Practices